Model predictive control sub-system power management

ABSTRACT

A system for controlling a plurality of electromechanical effectors operably connected to an engine to control engine parameters. The system also includes a plurality of sensors operably connected to measure a state or parameter of each effector, a power supply configured to supply power to the plurality of effectors, and a controller operably connected to the plurality of sensors, the plurality of effectors, and the power supply. The controller executes a method for an adaptive model-based control for controlling each effector, The method includes receiving a request indicative of a desired state for each effector, receiving a weighting associated each request, obtaining information about a current state of each effector, and updating an adaptive model based control (MBC) based upon the information. The method also includes generating a control command for an effector based upon the adaptive MBC and commanding the effector based upon the control command.

BACKGROUND

This disclosure relates to system control design, and more particularlyto model based predictive control and power management for mechanicalsystems.

Complex engineered systems including such things as vehicles, gasturbine engines, heating, ventilating, and air conditioning (HVAC)systems are coupled dynamic systems where response of one component mayinteract unfavorably with another component within the same system. Thisinteraction can be managed via two complementary approaches: controlmethods that actively manage the interaction between multiple components(or sub-systems) of a subsystem and, excess design margins that ensuresystem-level metrics are achievable in the presence of unfavorableinteractions. Advanced control design techniques can be useful inreducing excessive design margins (over-design) to either increasesystem performance or reduce its cost.

In electronic actuations systems used to manipulate mechanical systemssuch as variable geometry of a gas turbine engine, HVAC systems, andelevator systems, it becomes important to manage the amount of powerbeing drawn at any given time such that power supplies and powerdelivery system are not over drawn. In order to handle this, either thepower system must be sized to handle the worst case power draw of allsystem drawing at one time or the sub-systems have to be managedtogether, preventing the sum of the draw from exceeding limits. Since anengine is a highly coupled system, stopping or slowing one sub-systemwhile another is allowed to move at full rate can be problematictherefore coordinated management is required.

BRIEF DESCRIPTION

According to an embodiment, described herein is a method for controllinga plurality of electromechanical effectors of a gas turbine enginesystem. The method includes receiving a request indicative of a desiredstate for each effector of the plurality of effectors, receiving aweighting associated with each request, and obtaining information abouta current state and at least one previous state of each effector of theplurality of effectors; wherein the current state includes at least oneof an effector position and an effector current draw. The method alsoincludes updating model data information in an adaptive model basedcontrol (MBC) based upon the obtained information, generating at leastone control command for at least one effector of the plurality ofeffectors based upon the adaptive model based control, and commandingthe at least one effector of the plurality of effectors based upon thegenerated at least one control command.

In addition to one or more of the features described above, or as analternative to any of the foregoing embodiments, further embodiments mayinclude determining at least one of a constraint, an objective, anoperational parameter or characteristic, a weighting, and an initialcondition, for the adaptive model-based control system.

In addition to one or more of the features described above, or as analternative to any of the foregoing embodiments, further embodiments mayinclude adapting the adaptive model based control system based on the atleast one of the constraint, the objective, the initial condition, andthe model characteristic.

In addition to one or more of the features described above, or as analternative to any of the foregoing embodiments, further embodiments mayinclude that the adapting comprises modifying the model, constraints,and control parameters using information obtained about the currentstate of the effector.

In addition to one or more of the features described above, or as analternative to any of the foregoing embodiments, further embodiments mayinclude obtaining information about the current state and previous stateof the effector comprises obtaining information about at least one of:an effector position, an effector speed, an effector current, a sensor,a sensing system, and a total current or power for the plurality ofeffectors.

In addition to one or more of the features described above, or as analternative to any of the foregoing embodiments, further embodiments mayinclude that the generating step comprises identifying at least onereduced current requirement for the at least one effector of theplurality of effectors.

In addition to one or more of the features described above, or as analternative to any of the foregoing embodiments, further embodiments mayinclude a total current required for the plurality of effectors based onthe at least one reduced current requirement is less than a selectedthreshold.

In addition to one or more of the features described above, or as analternative to any of the foregoing embodiments, further embodiments mayinclude that the selected threshold is less than a cumulative currentrequirement for the plurality of effectors without the at least onereduced current requirement.

In addition to one or more of the features described above, or as analternative to any of the foregoing embodiments, further embodiments mayinclude the generating step comprises utilizing an quadratic programmingoptimizing method to determine the control command given the currentstate of the effector, the objective function, and the constraints andthe weightings.

In addition to one or more of the features described above, or as analternative to any of the foregoing embodiments, further embodiments mayinclude the effector is an electromechanical actuator driven from apower supply, the power supply having a current rating lower than thecumulative current ratings of the effectors.

In addition to one or more of the features described above, or as analternative to any of the foregoing embodiments, further embodiments mayinclude the electromechanical actuator includes a motor and a motorcontroller.

Also described herein in another embodiment is an adaptable model-basedcontrol system for controlling a plurality of electromechanicaleffectors of gas turbine engine. The system includes a plurality ofsensors operably connected to measure a state or parameter of eacheffector of the plurality of effectors, the plurality of effectorsoperably connected to the engine to control a plurality of engineparameters, a power supply configured to supply power to the pluralityof effectors, and a controller operably connected to the plurality ofsensors, the plurality of effectors, and the power supply, thecontroller configured to execute a method for an adaptive model-basedcontrol for controlling each effector. The method includes receiving arequest indicative of a desired state for each effector of the pluralityof effectors, receiving a weighting associated with each request, andobtaining information about a current state and previous states of eacheffector of the plurality of effectors. The current state includes atleast one of an effector position and an effector current draw. Themethod also includes updating model data information in an adaptivemodel based control (MBC) based upon the obtained information,generating at least one control command for at least one effector of theplurality of effectors based upon the adaptive model based control, andcommanding the at least one effector of the plurality of effectors basedupon the generated at least one control command.

In addition to one or more of the features described above, or as analternative to any of the foregoing embodiments, further embodiments mayinclude the controller determining at least one of a constraint, anobjective, an operational parameter or characteristic, a weighting, andan initial condition, for the adaptive model-based control system.

In addition to one or more of the features described above, or as analternative to any of the foregoing embodiments, further embodiments mayinclude the controller adapting the model based control system based onthe at least one of the constraint, the objective, the initialcondition, and the model characteristic, wherein the adapting comprisesmodifying the model, constraints, and control parameters usinginformation obtained about the current state of the effector.

In addition to one or more of the features described above, or as analternative to any of the foregoing embodiments, further embodiments mayinclude that the obtaining information about the current state andprevious state of the effector comprises obtaining information about atleast one of: an effector position, an effector speed, an effectorcurrent, a sensor, a sensing system, and a total current or total powerfor the plurality of effectors.

In addition to one or more of the features described above, or as analternative to any of the foregoing embodiments, further embodiments mayinclude that the updating step comprises updating at least one of: astate, a variable, a parameter, a constraint, an objective function, andan initial condition.

In addition to one or more of the features described above, or as analternative to any of the foregoing embodiments, further embodiments mayinclude that the generating step comprises identifying at least onereduced current requirement for the at least one effector of theplurality of effectors.

In addition to one or more of the features described above, or as analternative to any of the foregoing embodiments, further embodiments mayinclude a total current required for the plurality of effectors based onthe at least one reduced current requirement is less than a selectedthreshold.

In addition to one or more of the features described above, or as analternative to any of the foregoing embodiments, further embodiments mayinclude that the selected threshold is less than a cumulative currentrequirement for the plurality of effectors without the at least onereduced current requirement.

In addition to one or more of the features described above, or as analternative to any of the foregoing embodiments, further embodiments mayinclude that the generating step comprises utilizing a quadraticprogramming optimizing method to determine the control command given thecurrent state of the effector, the objective function, and theconstraints and the weightings.

In addition to one or more of the features described above, or as analternative to any of the foregoing embodiments, further embodiments mayinclude that the effector is an electromechanical actuator driven from apower supply, the power supply having a current rating lower than thecumulative current ratings of the effectors.

In addition to one or more of the features described above, or as analternative to any of the foregoing embodiments, further embodiments mayinclude the electromechanical actuator includes a motor and a motorcontroller.

Technical effects of the embodiments described include, but are notlimited to fault prediction, interactive monitoring, and powerefficiency of mechanical systems, in particular actuation systems.

BRIEF DESCRIPTION OF THE DRAWINGS

The subject matter which is regarded as the present disclosure isparticularly pointed out and distinctly claimed in the claims at theconclusion of the specification. The foregoing and other features, andadvantages of the present disclosure are apparent from the followingdetailed description taken in conjunction with the accompanying drawingsin which:

FIG. 1 depicts a simplified partial cutaway of a gas turbine engine asemployed in the embodiments;

FIG. 2 is a simplified block diagram of an engine control system inaccordance with an embodiment;

FIG. 3 illustrates a model based predictive monitoring system for amechanical system according to an embodiment;

FIG. 4 illustrates block diagram of model based predictive controlsystem for an effector according to an embodiment;

FIG. 5 illustrates a process of using a predictive system modelaccording to an embodiment.

DETAILED DESCRIPTION

Embodiments develop and utilize a decoupled design approach to componentdesign and its control, where each component (such as a motor) isover-sized or a common component is sized for worst case (such as thepower-supply) to accommodate unfavorable component interactions. Eachcomponent is then individually controlled, neglecting the interactionswith other components in the system. Further, each control design mayutilize any one of the classical control design methods or more recenttechniques that utilize predictions from a model.

Described herein in one or more embodiments is a method and system touse model predictive control to manage the engine sub-systems limitingthe overall current draw at any given time to a level that is acceptableto the power system. Weightings can be given to the sub-systems based onthe importance of the systems for a given maneuver or change inoperating condition. The weights can vary as needed to account fordifferent modes of gas turbine engine operation. For example, one set ofweighting may be required for an engine start, while a max poweracceleration may employ a different set. The control may be implementedutilizing a number of model predictive algorithms, including but notlimited to model predictive control, (MPC), or constrained model basedcontrol (CMBC). Also, in an embodiment, the control scheme utilizes realtime linearization of the sub-systems in order to deal with non-linearbehaviors of the system. Advantageously, the overall size of a systemmay be reduced utilizing smart management of power. Employing thedescribed embodiments facilitates reductions in size and ratings of thepower supply and conductors. Reduced current requirements and systemcontrol prevents the sub-systems to run to max rate or load and anygiven time. As a result, packaging is reduced, engine size and weightare reduced, and waste energy and waste heating is reduced.

For the purposes of promoting an understanding of the principles of thepresent disclosure, reference will now be made to the embodimentsillustrated in the drawings, and specific language will be used todescribe the same. It will nevertheless be understood that no limitationof the scope of this disclosure is thereby intended. The followingdescription is merely illustrative in nature and is not intended tolimit the present disclosure, its application or uses. It should beunderstood that throughout the drawings, corresponding referencenumerals indicate like or corresponding parts and features. As usedherein, the term controller refers to processing circuitry that mayinclude an application specific integrated circuit (ASIC), an electroniccircuit, an electronic processor (shared, dedicated, or group) andmemory that executes one or more software or firmware programs, acombinational logic circuit, and/or other suitable interfaces andcomponents that provide the described functionality.

Additionally, the term “exemplary” is used herein to mean “serving as anexample, instance or illustration”. Any embodiment or design describedherein as “exemplary” is not necessarily to be construed as preferred oradvantageous over other embodiments or designs. The terms “at least one”and “one or more” are understood to include any integer number greaterthan or equal to one, i.e. one, two, three, four, etc. The terms “aplurality” are understood to include any integer number greater than orequal to two, i.e. two, three, four, five, etc. The term “connection”can include an indirect “connection” and a direct “connection”.

As shown and described herein, various features of the disclosure willbe presented. Various embodiments may have the same or similar featuresand thus the same or similar features may be labeled with the samereference numeral, but preceded by a different first number indicatingthe figure to which the feature is shown. Thus, for example, element “a”that is shown in Figure X may be labeled “Xa” and a similar feature inFigure Z may be labeled “Za.” Although similar reference numbers may beused in a generic sense, various embodiments will be described andvarious features may include changes, alterations, modifications, etc.as will be appreciated by those of skill in the art, whether explicitlydescribed or otherwise would be appreciated by those of skill in theart.

FIG. 1 schematically illustrates a gas turbine engine 20. The gasturbine engine 20 is disclosed herein as a two-spool turbofan thatgenerally incorporates a fan section 22, a compressor section 24, acombustor section 26 and a turbine section 28. The fan section 22 drivesair along a bypass flow path B in a bypass duct, while the compressorsection 24 drives air along a core flow path C for compression andcommunication into the combustor section 26 then expansion through theturbine section 28. Although depicted as a two-spool turbofan gasturbine engine in the disclosed non-limiting embodiment, it should beunderstood that the concepts described herein are not limited to usewith two-spool turbofans as the teachings may be applied to other typesof turbine engines including three-spool architectures.

The exemplary gas turbine engine 20 generally includes a low speed spool30 and a high speed spool 32 mounted for rotation about an enginecentral longitudinal axis A relative to an engine static structure 36via several bearing systems 38. It should be understood that variousbearing systems 38 at various locations may alternatively oradditionally be provided, and the location of bearing systems 38 may bevaried as appropriate to the application.

The low speed spool 30 generally includes an inner shaft 40 thatinterconnects a fan 42, a low pressure compressor 44 and a low pressureturbine 46. The inner shaft 40 is connected to the fan 42 through aspeed change mechanism, which in exemplary gas turbine engine 20 isillustrated as a geared architecture 48 to drive the fan 42 at a lowerspeed than the low speed spool 30. The high speed spool 32 includes anouter shaft 50 that interconnects a high pressure compressor 52 and highpressure turbine 54. A combustor 56 is arranged in exemplary gas turbineengine 20 between the high pressure compressor 52 and the high pressureturbine 54. An engine static structure 36 is arranged generally betweenthe high pressure turbine 54 and the low pressure turbine 46. The enginestatic structure 36 further supports bearing systems 38 in the turbinesection 28. The inner shaft 40 and the outer shaft 50 are concentric androtate via bearing systems 38 about the engine central longitudinal axisA which is collinear with their longitudinal axes.

The core airflow is compressed by the low pressure compressor 44 thenthe high pressure compressor 52, mixed and burned with fuel in thecombustor 56, then expanded over the high pressure turbine 54 and lowpressure turbine 46. The turbines 46, 54 rotationally drive therespective low speed spool 30 and high speed spool 32 in response to theexpansion. It will be appreciated that each of the positions of the fansection 22, compressor section 24, combustor section 26, turbine section28, and fan drive gear system 48 may be varied. For example, gear system48 may be located aft of combustor section 26 or even aft of turbinesection 28, and fan section 22 may be positioned forward or aft of thelocation of gear system 48.

The engine 20 in one example is a high-bypass geared aircraft engine. Ina further example, the engine 20 bypass ratio is greater than about six(6), with an example embodiment being greater than about ten (10), thegeared architecture 48 is an epicyclic gear train, such as a planetarygear system or other gear system, with a gear reduction ratio of greaterthan about 2.3 and the low pressure turbine 46 has a pressure ratio thatis greater than about five. In one disclosed embodiment, the engine 20bypass ratio is greater than about ten (10:1), the fan diameter issignificantly larger than that of the low pressure compressor 44, andthe low pressure turbine 46 has a pressure ratio that is greater thanabout five 5:1. Low pressure turbine 46 pressure ratio is pressuremeasured prior to inlet of low pressure turbine 46 as related to thepressure at the outlet of the low pressure turbine 46 prior to anexhaust nozzle. The geared architecture 48 may be an epicycle geartrain, such as a planetary gear system or other gear system, with a gearreduction ratio of greater than about 2.3:1. It should be understood,however, that the above parameters are only exemplary of one embodimentof a geared architecture engine and that the present disclosure isapplicable to other gas turbine engines including direct driveturbofans.

A significant amount of thrust is provided by the bypass flow B due tothe high bypass ratio. The fan section 22 of the engine 20 is designedfor a particular flight condition—typically cruise at about 0.8 Mach andabout 35,000 feet (10,688 meters). The flight condition of 0.8 Mach and35,000 ft (10,688 meters), with the engine at its best fuelconsumption—also known as “bucket cruise Thrust Specific FuelConsumption (‘TSFC’)”—is the industry standard parameter of lbm of fuelbeing burned divided by lbf of thrust the engine produces at thatminimum point. “Low fan pressure ratio” is the pressure ratio across thefan blade alone, without a Fan Exit Guide Vane (“FEGV”) system. The lowfan pressure ratio as disclosed herein according to one non-limitingembodiment is less than about 1.45. “Low corrected fan tip speed” is theactual fan tip speed in ft/sec divided by an industry standardtemperature correction of [(Tram ° R)/(518.7° R)]0.5. The “Low correctedfan tip speed” as disclosed herein according to one non-limitingembodiment is less than about 1150 ft/second (350.5 m/sec).

FIG. 2 illustrates an exemplary embodiment of an engine control system100 as may be employed with engine 20. It should be appreciated thatwhile the engine control system 100 as depicted and described herein maybe a model based control system, other configurations and architecturesare possible. As shown in FIG. 2, system 100 includes engine 20, anactuator 114 and a sensor 108 that is communicatively coupled with aprocessor or controller 106. Sensor 108 is any of a variety of sensoremployed in the engine including temperature, pressure, flow, speed andposition sensors, and the like. In this embodiment, and for the purposesof description of the embodiments herein the sensor 108 is a positionsensor associated with one or more of the actuators abut other types ofsensors (e.g., flow meters and speed sensors) also could be used.

The processor 106 is communicatively coupled to the actuator 114 toprovide commands to control the engine 20. In an embodiment onceinstance of the actuator 114 may include a motor drive 116 and motor 118as may be employed with an electromechanical actuator. In someembodiments the actuator 114 could include solenoids, servovalves,hydraulic actuators, and the like. In addition the processor 106 isoperatively coupled to a memory 110, sensor and actuator models 104, andan engine model 102. The sensor and actuator models 104 are associatedwith any of the sensor(s) 108 and actuators 114, and, in thisembodiment, are communicatively coupled with the engine model 102.Alternatively, functionality associated with a sensor and actuatormodels 104 may be an integrated with an engine model 102 in otherembodiments. Further, in other embodiments, engine model 102 and/orsensor and actuator model 104 may be integrated into various componentssuch as, for example, into a Full Authority Digital Engine Control(FADEC) system such as system 100. In an exemplary embodiment, the FADECmay be physically attached to the gas turbine engine 20.

In operation, the sensor 108 monitors an engine operating parameter,such as temperature, pressure, position, and the like, and provides datacorresponding to the parameter to the controller 106, which may storethe data in memory 110. For example, in an embodiment, the sensor 108measures the position and/or speed of the actuator 114 to provide datato the controller 106 regarding the motion of the actuator 114. Theprocessor 106 processes the data stored in the memory 110 and employsthe data in various control algorithms, prognostics, diagnostics and thelike. In some embodiments, the processor 106 compares from the sensor108 to corresponding data of the sensor and actuator model 104. If thedifference between the measured data of the sensor 108 and the referencedata of the actuator model 104 is outside of a threshold value, theprocessor 106 may take various steps to address the difference includingupdate the sensor and actuator model 104 with the data of the sensor108, as discussed further herein. In an embodiment, by updating thereference data of the engine model 102 or actuator model 104,degradation of the components, which may occur over time, can beaccommodated.

Monitoring engine parameter data provides the basis for performing gasturbine engine performance tracking. The dynamic behavior of measurementdevices, particularly detecting and quantifying the changes in thedynamic responses of measurement devices, e.g., sensors 108, is usefulin performing gas turbine engine performance tracking. By monitoringsensor data based on transient behavior, steady state behavior, andtrend data, aspects of operation or even degradation of engine actuators114 may be detected that may not be perceived when the engine isoperating at steady state alone. Ascertaining and distinguishingperformance trends may allow the predictive models 200, 150 or theengine model 102 and sensor and actuator model 104 to be updated inorder to improve sensor 108 and actuator 114 operation, performance andfurther potentially compensate sensor 108 or actuator 114 degradation.

The described embodiments include adaptive model-based control systems100 and adaptive control processes 200, 150 implementing the model basedcontrol. These systems 100 and methods 200, 150 may be employed toimprove the performance of system components e.g., actuators 114 as wellas to detect deterioration and/or degradation, faults, failures ordamage to an engine 20, and/or components thereof including sensors 108and actuators 114. Furthermore, the described embodiments facilitateincorporating such information into the various models, optimizations,objective functions, constraints and/or parameters in the control system100 to allow the control system 100 to improve performance and/oroperability as possible given the current engine 20 condition or thecondition of a component thereof such as, but not limited to actuator114. Current data regarding the dynamic characteristics of the sensors108 and actuators 114 providing this information is useful inmaintaining appropriate control. Particularly, it is desirable forsensor and actuator models 104 and predictive model 150 to modelactuators 114 and accommodate limited power availability based on asmaller lower current capability power supply 130. This accommodation isaccomplished by updating the model 150 in the model-based control systemwith information regarding the states of the actuators 114 as well asthe status of the power supply 130 (e.g., current load for the actuators114). The predictive engine models 200 and predictive actuator model 150in the control system 100 may be adapted by using a filter, trackingfilter, logic or other suitable method to modify states, variables,quality parameters, scalars, adders, constraints, limits or any otheradaptable parameter so that the performance and limitations of the modelmatch that of the engine 20 or actuator 114 after the parameter ischanged. It should be appreciated the while for the purposes ofdiscussion each of the models 200 and 150 as well as engine model 102and sensor and actuator model 104 and the control system 100 aredescribed separately and as separate entities, such description ismerely for illustration. The models could be separated or integratedwithout distinction or deviation from the disclosure herein. Forexample, in one embodiment, the engine model, 102, and sensor andactuator model 104 may each be integral parts of the adaptive controlmethod 200 or 150 of controller 106. Moreover, in some embodiments, themodels may be integral with other components such as the actuator 114,or another controller employed in a particular embodiment.

Engine models 102 and sensor and actuator models 104 also provide thereference level for performing gas turbine engine performance trackingand anomaly detection to monitor engine 20 and engine sensor 108 andactuator 114 performance function, and even deterioration. Such trackingsupports maintenance operations and logistics as well as alertingoperators of potential failure conditions. This information reducesexposure to in-operation shutdowns and unscheduled maintenance orremovals. It should be appreciated that not every eventuality is likelyto be covered in the predictive models 200, 150 as employed, it isdesirable for such models 200, 150 to reconfigure, adapt, and learn tomake predictions or corrections based on various engine parameters, andconditions. In some embodiments, such adaptability for normal ordeteriorated conditions comes from using an estimator or tracking filterto modify model inputs, outputs, or interior parameters as conditionschange. Such adaptability results from using diagnostics that can selectbetween different models reference models for the engine 102, or sensorsand actuators 104, modify model inputs, outputs, or interior parameters,or can modify the optimizations, objective functions, constraints,and/or parameters in the control system 100. Therefore, the embodimentsdescribed below allow for the trend data regarding the response ofengine sensors and actuators 108, 114 to be determined. These trend dataprovide for the detection of component and specifically actuatoranomalies that may not typically be detectable, yet aid in theprediction of degradation or failures. As a result, adjustment and/orcorrection of engine models 102 and sensor and actuator models 104 tocompensate for degradation in one or more sensors 108 and actuators 114is facilitated.

In embodiments described, any physical system, control system 100 orproperty of the engine 20 or engine subsystem may be modeled, including,but not limited to, the engine itself, the gas path and gas pathdynamics; sensors 108, actuators 114, effectors, or other controllingdevices that modify or change any engine behavior. The models 102, 104of these components and/or systems may be physics-based models(including their linear approximations). Additionally or alternatively,the models may be based on linear and/or nonlinear systemidentification, neural networks, and/or combinations of all of these.

Gas turbine engines such as engine 20 due to the large range ofoperating conditions and power levels experienced during operation mayrequire complex modeling techniques. Logically, turbine operation isrestricted due to mechanical, aerodynamic, thermal and flow limitations.Moreover, advantageously Model Predictive Controls (MPC) are ideal forsuch environments because they can specifically address thenonlinearities, and both the input and output constraints of manyvariables, all in a single control formulation. Model predictivecontrols are full state feedback controls that use a model of theprocess/system/component to predict the output, up to a certain instantof time, based on the inputs to the system and the most recent processmeasurements. Model Inverting controls are full state feedback controlsthat use an inverted model of the process/system/component (engine,actuator, sensor, and the like) to formulate and generate referencecommands based on actual measured conditions. Model Inverting controlinput values are calculated by inverting the dynamical equations thatrelate the actuator inputs to the desired outputs pertaining to variouscontrol objectives, given knowledge of the full state of the system. Inaddition, these calculations account for various constraints associatedwith the effectors e.g. actuators 114 and for performance and operationrelated limits associated with key engine parameters.

In the embodiments described, as an example, a multi-variableConstrained Model-Based Control (CMBC) architecture is employed incontrol system 100 for controlling the engine 20 and the actuators 114,the control scheme may be based on Model Predictive Control (MPC) orModel Inverting Control (MIC) techniques. These controls can bemultiple-input multiple-output (MIMO) to account for interactions of thecontrol loops, they can be model-based, and they can have limits orconstraints built as an integral part of the control formulation andoptimization to avoid designing controllers for each limit.

The controller 106 can include a memory system 110 to store instructionsthat are executed by one or more processors 112. The executableinstructions may be stored or organized in any manner and at any levelof abstraction, such as in connection with a controlling and/ormonitoring operation of the sensor system 108 or actuators 114. The oneor more processors 112 can be any type of central processing unit (CPU),including a microprocessor, a digital signal processor (DSP), amicrocontroller, an application specific integrated circuit (ASIC), afield programmable gate array (FPGA), or the like. Also, in embodiments,the memory system 110 may include random access memory (RAM), read onlymemory (ROM), or other electronic, optical, magnetic, or any othercomputer readable medium onto which is stored data and algorithms in anon-transitory form. The controller 106 can also interface with acommunication bus 120 to send and receive data values and/or executableinstructions. The controller 106 can include other interfaces (notdepicted), such as various outputs, wireless communication interfaces,and the like.

Embodiments of the system 100 may include predictive models 200 for theengine 20, and 150 for the effectors or actuators 114 executable by oneor more processors 112, where the predictive models 200, 150 include aplurality of component models 102, 104 configured to correspond with amodeled state of the mechanical system 100 and components thereof. Forexample, an engine model 102 and a model of an effector or actuator 104.Similarly, models for sensors and other components may also be employed.As further described herein, the predictive model 200, 150 can be usedto generate prediction results regarding future states of the mechanicalsystem 100, and in particular the engine 20, sensors 108 and actuators114. The system 100 and controller 106 can capture sensor data from thesensor system 108 as observed history, which may also capture datasnapshots surrounding operations, data trends, detected failures,abnormal conditions, and/or other targeted conditions. The observedhistory can be used real-time or offline to further refine thepredictive model(s) 200, 150 and develop/improve operational and failuremode definitions.

FIG. 3 depicts a simplified diagram of a portion of an engine controlsystem 100 with model predictive control methodology 500 for sub-systempower control in accordance with an embodiment. Conventionally, asdescribed herein, engine controller 106 may be configured to providecommands to various effectors or actuator(s) shown generally here as 114and more specifically as actuators 114 a, 114 b, . . . 114 n. Forexample, actuators to control guide vanes, bleed valves, fuel nozzlesand the like on the engine 20. The actuator 114 may be comprised of anelectromechanical type actuators having, in some configurations, a motorcontroller, shown generally as 116 and specifically as motorcontroller(s) 116 a, 116 b, . . . 116 n and a motor shown generally as118 and specifically a 118 a, 118 b, . . . 118 n. It should beappreciated that while depicted as a separate component, such depictionis for illustrative purposes only. In some embodiments, the motorcontroller 116 could be an integral component with engine controller 106and/or integral with the motor 118. Conventionally, the enginecontroller 106 may provide motor command signals shown generally as 119and more specifically as 119 a, 119 b, . . . 119 n to the motorcontroller 116 a, 116 b, . . . 116 n. The motor controller 116 mayreceive control commands 119 from the engine controller 106 as well asfeedback signals from the motor 118 and provide control loop closure forthe motor 118. Moreover, the motor commands 119 may be made directly tothe motor 118 and directly commanding the motor 118 via the enginecontroller 106.

In an embodiment a power denoted 150 is interposed between thecontroller 106 an the actuators 114 a, 114 b, . . . 114 n. In anembodiment, the engine controller 106 provides motor command signalsshown generally as 119 as a demand to the power controller 150. Thepower controller 150 executes a methodology 500 to implement a modelpredictive control algorithm based on the demand signals 119 generatesmodified motor command signals shown generally as 151 and morespecifically as 151 a, 151 b, . . . 151 n, which are then directed tothe respective the motor controller(s) 116 a, 116 b, . . . 116 n. Themotor controller(s) 116 may receive motor commands 151 from the powercontroller 150 as well as feedback signals from the motor 118 andprovide control loop closure for the motor 118. Moreover, the motorcommands 119, 151 may be made directly to the motor 118 directlycommanding the motor 118 via the engine controller 106. The motorcontroller 116 a, 116 b, . . . 116 n may also receive power from a powersupply 130 and supply excitation power to the motor controller 116. Itshould be appreciated, that while in an embodiment, the feedback fromthe motor 118 a, 118 b, . . . 118 n is depicted as being directed to therespective motor controller(s) 116 a, 116 b, . . . 116 n, such depictionis merely illustrative. As depicted, the position and speed feedback 115for each of the motors 118 could also be directed to the power manager150. In addition the motor controllers 116 a, 116 b, . . . 116 n couldbe integral with the power manager 150 including the control loops forcontrolling the motors 118 a, 118 b, . . . 118 n.

Continuing with FIG. 3, in an embodiment a power manager 150 employingmodel based control methodology 500 may be employed to manage the poweremployed by the actuators 114. In an embodiment, a plurality of motorcommand signals 119 a, 119 b, . . . 119 n for each of the actuators 114e.g., 114 a, 114 b, . . . 114 n from the engine controller 106 arerouted to the power manager 150. In addition, a plurality of weightingfactors 107 correlated to the sensitivities of the motor commands (e.g.,119) are provided by the engine controller 106 to the power manager 150.That is, under conventional control, the engine controller 106 may makeone or more commands to the actuators 114 e.g., 114 a, 114 b, . . . 114n, where the commands are effectively uncoupled and each given the sameweighting 107. As a result, each of the actuators 114 e.g., 114 a, 114b, . . . 114 n (and in particular, the motor controllers 116 e.g., 116a, 116 b, . . . 116 n are fully demanding power from the power supply130 as needed to satisfy the individual motor commands e.g., 151 a, 151b, . . . 151 n for each respective actuator 114, e.g., 114 a, 114 b, . .. 114 n. Conversely, in an embodiment, for example, under selectedoperating conditions, the engine control 106 may establish weightingfactors 107 associated with one or more of the motor commands 119 e.g.,119 a, 119 b, . . . 119 n. The weighting factors 107 permit the modelbased control algorithms of the power manager 150 to optimize and managethe current draw by the motor controllers 116 a, 116 b, . . . 116 n onthe power supply 130. In an embodiment, an operating mode of the engine20 may alter the selected weighting(s) 107 corresponding to each of theeffectors as needed. For example, primary effectors (e.g., actuators114) may be given the highest weighting 107, while secondary systems andfunctions may be given lower weightings 107. For example, in normaloperation, the highest weighting 107 may be given to variable vanesystems, fuel management, and nozzle operation, while lower weighting107 may be assigned to engine bleeds, and external air flow management.Conversely, under selected conditions for the engine 20, the weightings107 may be modified. For example, under engine starting conditions;bleed valves and guide vanes may have high weighting 107 while nozzlesto be lowered. Likewise, under low power engine acceleration; nozzleweighting 107 may be lowered in favor of increasing weighting 107 forguide vanes. Further, under engine stall management conditions; bleedvalves may be given the highest weighting 107, while under degradedoperational conditions, guide vane weighting 107 may be lowered in favorof nozzles.

In an embodiment, the current drawn from the power supply 130 can bemanaged to within a selected threshold. In another embodiment, thecurrent may be managed to limit the current draw for any given actuators114 (e.g., 114 a, 114 b, 114 n) to a selected level. The power manager150 also receives one or more current feedback signals 131 correspondingto the current draw associated with each actuator 114 e.g., 114 a, 114b, . . . 114 n. In an embodiment, an average aggregate current providedby the power supply 130 is employed. The power manager 150 employs modelpredictive control techniques to formulate and adjust the motor commands151, e.g., 151 a, 151 b, . . . 151 n for each of the actuators 114 e.g.,114 a, 114 b, . . . 114 n. Advantageously, the described approachpermits a system designer to employ a selected rating power supply andconductors, rather than designing to the worst case cumulative powerdraw requirements associated with each actuators 114 e.g., 114 a, 114 b,. . . 114 n.

Continuing with FIG. 3, because each actuator 114 is differentoperationally, deteriorates, degrades, experience faults, or be damagedin different ways, the model 104 should be able to track or adapt itselfto follow such changes. The actuator model 104 should preferably revealcurrent information about how a particular actuator 114 is operating ata given time including the operation of its motor controller 116 andmotor 118, and more particularly the current required for each from thepower supply 130. This allows the future behavior of the actuator 114 tobe more accurately predicted. Actuator parameters and states are twoareas of the predictive model method 500 and/or actuator model 104 thatcan be modified to match the actuator model 104 to the actual actuator114. In one embodiment, and particularly in more complex systems, aparameter estimator may be used to determine various actuatorparameters, and a state estimator may be used to determine the states. Aparameter estimator estimates and modifies parameters in the predictivemodel 150 and/or actuator model 104 to reduce the error between thesensors 108 and the models of sensors in the 104. This is calledtracking the model to the actual component and, in particular, theactuator 114. A state estimator may be used to further aid in trackingthe model 104 to the actuator 114. The state information may also beused to initialize the model-based predictive control method 500 at eachtime interval.

Using model based predictive control(s) methodology 500 allows the powermanager 150 and therefore the engine control system 100 to use all theinformation provided by the actuator model 104, estimator, anddiagnostic processes. The algorithm used herein allows the controllere.g. power manager 150 and/or engine controller 106 to see what theactuator 114 is going to do over time and at the same time know theactuator operating constraints. The engine control system 100 via theengine controller 106 or power manager 150 can then modify all of thecontrol actions to ensure that none of the constraints are violatedwhile satisfying a given control objective. In other word, the controlideally can develop an improved, if not the best possible, solution tomeet the system requirements within the constraints presented.

MPC, in this instance, is based on the constrained open-loopoptimization of a finite objective function. This optimization commencesfrom an assumed known initial state and uses a dynamic system model todescribe the evolution of the outputs. The objective function is amathematical way of defining the goal of the control system. Theobjective function determines what is defined as optimal. Some generalobjective functions are to: minimize current consumption, maximizepower, torque, and speed, maximize component life, minimize stress,minimize drag, follow reference current, speed, or torque commands, orminimize or maximize actuator command(s), minimize dollars, and/orminimize costs. The optimization algorithm used inside the control canbe constrained or unconstrained, linear or non-linear.

Turning now to FIG. 4 and continuing with FIG. 3 as well, for details ofthe engine control system 100 and method of model based predictivecontrol 104 as employed with an effector (e.g., actuator 114) as part ofthe function of the power manager 150. The MPC function of the powermanager 150 receives the effector request, which in this embodiment isthe motor command(s) 119 for the various actuator(s) 114. As describedearlier, the model based control method 500 of the power manager 150also receives feedback 115, 131 either, directly from the motors 118,motor controller 116, and the power supply 130. The feedback 115, 131 isapplied to a linear model 104 of the particular effector e.g., theactuators 114. The feedback and the output of the linear model 104 andthen directed to the solver 156. It should be appreciated that while alinear model 104 is depicted, other types of models may be employed.

In an embodiment, the model predictive control of method 500 solves aquadratic programming optimization problem to determine an optimalcommand of the manipulated variables, in this instance, the motorcommand(s) 151 to the motor controller(s) 116 as a function of the powerrequirements for the actuators 114. In operation, the solver solutionprocess is initiated with an initial “guess” as an established initialcondition. The solver 156 iterates to the optimal solution within theconstraints 154 of the engine control system 100. The method 500 allowsfor both hard and soft constraints to direct the solution. Hardconstraints are constraints 154 that cannot be exceeded and may have todo with physical and dynamic limitations of the hardware, system, andthe like. For example actuator strokes and rate limitations, powercapabilities and the like. Likewise soft constraints are designlimitations such as may be associated with the process constraints andlimitations associated the system or components. The model basedpredictive control method 500 also allows assignment of weighting (e.g.,107) when determining best solution. This approach facilitates, andforces tightened control of some effectors (e.g., actuators 114) withinthe plant at the expense of others.

Advantageously, the defined weighting 107 provided by the enginecontroller 106 provides guidance to the power manager 150 on whicheffectors (e.g., actuators 114) need accurate control for a givenoperating condition. The engine controller 106 and its and logicdetermine engine mode of operation. In an embodiment, the operating modeof the engine 20 identifies which weighting 107 associated with aselected effector set (e.g., actuators 114 a, 114 b, . . . 114 n) is tobe employed. For example, in an embodiment a high relative weighting 107indicates tight accuracy is required. Under such conditions, primaryeffectors (e.g., actuators) receive highest weighting 107, whilesecondary systems have lower weighting 107. For example, in oneembodiment, highest weighting 107 is given to variable vane systems,fuel management, e.g., nozzles, while lowest weighting 107 is given toengine bleeds, external air flow management.

FIG. 5 depicts a flowchart of the methodology of model based predictivecontrol for an effector or actuator 114 shown generally as 500. As maybe executed by the engine controller 106 and/or power manager 150.Initially as shown at process step 505 the method receives the enginecontrol loop actuator commands 119 with associated effector weightings107. At process step 510, the sensor measurements from sensor 108 andactuator(s) 114 and information regarding the current state of thecontrol system 100 are received for each effector (e.g., actuator 114)by the power manager 150. At process block 515, method 500 uses thespecific information related to effectors, (e.g., actuator 114) currentstate including position speed, voltage, and current, power in order toadapt the MBC data and models to the new associated constraints 154. Thedescribed enhancements to the control system 100 operate to periodicallyupdate actuator status, health, and constraint information in the MBC ofthe control system 100. That is, the controller (e.g., 106) operatesunder selected conditions to update its actuator constraints (related toposition, rate, and bandwidth, operation constraints, current draw andthe like). Moreover, the control system 100 can now use the updatedconstraints, (e.g., range, rate of operation) and coordinate it with theother actuators (e.g., 114 a, 114 b, . . . 114 m) to optimize thecumulative current loading on the power supply 130.

As depicted at process step 515, the MBC algorithms in the controlsystem 100 may then adapt to a new configuration of employing theactuator 114 that is exhibiting new constraints 154. Adaptation in oneinstance may mean using new constraints information 154, with each newactuator command (e.g., 119 a, 119 b, 119 n) while maintaining the samecontrol architecture. Depending on the state information, constraints154, and weightings 107, and the like, the objective or object functionof the MBC can be changed as well to accommodate the commandrequirements for the actuators 114 to achieve desired engine performanceand yet remain within the specifications of the power supply 130. Usingmodel based control(s) allows the control system 100 to use more or allof the information provided by the system model, estimators, sensors108, and diagnostic algorithms. Model based predictive control uses themodel (e.g. 200, 150) and the current state information in the controlto predict the future behavior of the effector (e.g. actuator 114).Because a prediction of the future behavior can be formed given anevolution of control inputs, many different control inputs can be testedto see which ones will track the desired references (e.g., speeds,positions, current draw, etc.), while still obeying any operatingconstraints 154 (e.g., limits on the actuators, maximum supply current,etc.). The algorithm used herein allows the controller 106 and/or powermanager 150 to see what the actuator 114 is going to do over the futuretime horizon, and at the same time know the operating constraints forall the effectors/actuators 114. The control method 500 can then modifyall of the control commands e.g., 151) to ensure that none of theconstraints 154 are violated while optimally satisfying a given controlobjective (including the control commands for each actuator 114 from theengine controller 106 as depicted at process step 520. This means thatthe control can develop the best possible solution to meet the systemrequirements and avoid excessive current draw on the power supply 130.Finally, as depicted at process step 525 the effectors/actuators 114 areeach commanded based on the adaptive MBC.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the embodiments.While the present disclosure has been described in detail in connectionwith only a limited number of embodiments, it should be readilyunderstood that the present disclosure is not limited to such disclosedembodiments. Rather, the present disclosure can be modified toincorporate any number of variations, alterations, substitutions orequivalent arrangements not heretofore described, but which arecommensurate with the spirit and scope of the present disclosure.Additionally, while various embodiments of the present disclosure havebeen described, it is to be understood that aspects of the presentdisclosure may include only some of the described embodiments.Accordingly, the present disclosure is not to be seen as limited by theforegoing description, but is only limited by the scope of the appendedclaims.

What is claimed is:
 1. A method for controlling a plurality ofelectromechanical effectors of a gas turbine engine system, the methodcomprising: receiving a request indicative of a desired state for eacheffector of the plurality of effectors; receiving a weighting associatedwith each request; obtaining information about a current state and atleast one previous state of each effector of the plurality of effectors;wherein the current state includes at least one of an effector positionand an effector current draw; updating model data information in anadaptive model based control (MBC) based upon the obtained information;generating at least one control command for at least one effector of theplurality of effectors based upon the adaptive model based control; andcommanding the at least one effector of the plurality of effectors basedupon the generated at least one control command.
 2. The method of claim1, further including determining at least one of a constraint, anobjective, an operational parameter or characteristic, a weighting, andan initial condition, for the adaptive model-based control system. 3.The method of claim 2, further including adapting the adaptive modelbased control system based on the at least one of the constraint, theobjective, the initial condition, and the model characteristic.
 4. Themethod of claim 3, wherein the adapting comprises modifying the model,constraints, and control parameters using information obtained about thecurrent state of the effector.
 5. The method of claim 1, whereinobtaining information about the current state and previous state of theeffector comprises obtaining information about at least one of: aneffector position, an effector speed, an effector current, a sensor, asensing system, and a total current or power for the plurality ofeffectors.
 6. The method of claim 1, wherein the generating stepcomprises identifying at least one reduced current requirement for theat least one effector of the plurality of effectors.
 7. The method ofclaim 6, wherein a total current required for the plurality of effectorsbased on the at least one reduced current requirement is less than aselected threshold.
 8. The method of claim 6, wherein the selectedthreshold is less than a cumulative current requirement for theplurality of effectors without the at least one reduced currentrequirement.
 9. The method of claim 1, wherein the generating stepcomprises utilizing an quadratic programming optimizing method todetermine the control command given the current state of the effector,the objective function, and the constraints and the weightings.
 10. Themethod of claim 1, wherein the effector is an electromechanical actuatordriven from a power supply, the power supply having a current ratinglower than the cumulative current ratings of the effectors.
 11. Themethod of claim 1, wherein the electromechanical actuator includes amotor and a motor controller.
 12. An adaptable model-based controlsystem for controlling a plurality of electromechanical effectors of gasturbine engine, the system comprising: a plurality of sensors operablyconnected to measure a state or parameter of each effector of theplurality of effectors, the plurality of effectors operably connected tothe engine to control a plurality of engine parameters; a power supplyconfigured to supply power to the plurality of effectors; and acontroller operably connected to the plurality of sensors, the pluralityof effectors, and the power supply, the controller configured to executea method for an adaptive model-based control for controlling eacheffector, the method comprising: receiving a request indicative of adesired state for each effector of the plurality of effectors; receivinga weighting associated with each request; obtaining information about acurrent state and previous states of each effector of the plurality ofeffectors; wherein the current state includes at least one of aneffector position and an effector current draw; updating model datainformation in an adaptive model based control (MBC) based upon theobtained information; generating at least one control command for atleast one effector of the plurality of effectors based upon the adaptivemodel based control; and commanding the at least one effector of theplurality of effectors based upon the generated at least one controlcommand.
 13. The system of claim 12, further including the controllerdetermining at least one of a constraint, an objective, an operationalparameter or characteristic, a weighting, and an initial condition, forthe adaptive model-based control system.
 14. The system of claim 12,further including the controller adapting the model based control systembased on the at least one of the constraint, the objective, the initialcondition, and the model characteristic, wherein the adapting comprisesmodifying the model, constraints, and control parameters usinginformation obtained about the current state of the effector.
 15. Thesystem of claim 12, wherein the obtaining information about the currentstate and previous state of the effector comprises obtaining informationabout at least one of: an effector position, an effector speed, aneffector current, a sensor, a sensing system, and a total current ortotal power for the plurality of effectors.
 16. The system of claim 12,wherein the updating step comprises updating at least one of: a state, avariable, a parameter, a constraint, an objective function, and aninitial condition.
 17. The system of claim 12, wherein the generatingstep comprises identifying at least one reduced current requirement forthe at least one effector of the plurality of effectors.
 18. The system,of claim 17, wherein a total current required for the plurality ofeffectors based on the at least one reduced current requirement is lessthan a selected threshold.
 19. The system of claim 17, wherein theselected threshold is less than a cumulative current requirement for theplurality of effectors without the at least one reduced currentrequirement.
 20. The method of claim 12, wherein the generating stepcomprises utilizing an quadratic programming optimizing method todetermine the control command given the current state of the effector,the objective function, and the constraints and the weightings.
 21. Themethod of claim 12, wherein the effector is an electromechanicalactuator driven from a power supply, the power supply having a currentrating lower than the cumulative current ratings of the effectors. 22.The method of claim 21, where the electromechanical actuator includes amotor and a motor controller.